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efficientnet_v2_m.py
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efficientnet_v2_m.py
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import torch
import torch.nn
import torch.functional
import torch.nn.functional
class efficientnet_v2_m(torch.nn.Module):
def __init__(self):
super().__init__()
self.features_0_0 = torch.nn.modules.conv.Conv2d(3, 24, 3, 2, 1, bias=False)
self.features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_1_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_1_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False)
self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False)
self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(24, 24, 1, 1, 0, bias=False)
self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(24, 96, 3, 2, 1, bias=False)
self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(96, 48, 1, 1, 0, bias=False)
self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 1, 1, bias=False)
self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(192, 48, 1, 1, 0, bias=False)
self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(48)
self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(48, 192, 3, 2, 1, bias=False)
self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(192, 80, 1, 1, 0, bias=False)
self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 3, 1, 1, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(80, 320, 1, 1, 0, bias=False)
self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(320, 320, 3, 2, 1, groups=320, bias=False)
self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(320)
self.features_14_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_14_conv_6_fc_0 = torch.nn.modules.linear.Linear(320, 24)
self.features_14_conv_6_fc_2 = torch.nn.modules.linear.Linear(24, 320)
self.features_14_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_14_conv_7 = torch.nn.modules.conv.Conv2d(320, 160, 1, 1, 0, bias=False)
self.features_14_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_15_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_15_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_15_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_15_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_15_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_15_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_16_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_16_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_16_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_16_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_16_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_16_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_17_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_17_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_17_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_17_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_17_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_17_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_18_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_18_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_18_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_18_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_18_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_18_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_19_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_19_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_19_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_19_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_19_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_19_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(160, 640, 1, 1, 0, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(640, 640, 3, 1, 1, groups=640, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_20_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_20_conv_6_fc_0 = torch.nn.modules.linear.Linear(640, 40)
self.features_20_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 640)
self.features_20_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_20_conv_7 = torch.nn.modules.conv.Conv2d(640, 160, 1, 1, 0, bias=False)
self.features_20_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(160)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(160, 960, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(960)
self.features_21_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_21_conv_6_fc_0 = torch.nn.modules.linear.Linear(960, 40)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(40, 960)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(960, 176, 1, 1, 0, bias=False)
self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_22_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_22_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_23_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_23_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_24_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_24_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_25_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_25_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_26_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_26_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_27_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_27_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_28_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_28_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_29_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_29_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_30_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_30_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_31_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_31_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_32_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_32_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_33_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_33_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 1, 1, groups=1056, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_34_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_34_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(1056, 176, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(176)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(176, 1056, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(1056, 1056, 3, 2, 1, groups=1056, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1056)
self.features_35_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_35_conv_6_fc_0 = torch.nn.modules.linear.Linear(1056, 48)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1056)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(1056, 304, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_36_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_36_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_37_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_37_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_38_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_38_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_39_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_39_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_40_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_40_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_41_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_41_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_41_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_41_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_41_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_41_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_41_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_41_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_41_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_41_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_42_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_42_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_42_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_42_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_42_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_42_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_42_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_42_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_42_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_42_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_43_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_43_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_43_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_43_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_43_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_43_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_43_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_43_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_43_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_43_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_44_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_44_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_44_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_44_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_44_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_44_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_44_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_44_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_44_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_44_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_45_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_45_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_45_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_45_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_45_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_45_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_45_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_45_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_45_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_45_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_46_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_46_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_46_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_46_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_46_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_46_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_46_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_46_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_46_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_46_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_47_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_47_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_47_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_47_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_47_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_47_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_47_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_47_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_47_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_47_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_48_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_48_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_48_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_48_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_48_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_48_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_48_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_48_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_48_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_48_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_49_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_49_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_49_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_49_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_49_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_49_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_49_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_49_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_49_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_49_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_50_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_50_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_50_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_50_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_50_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_50_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_50_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_50_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_50_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_50_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_51_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_51_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_51_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_51_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_51_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_51_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_51_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_51_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_51_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_51_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_52_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_52_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_52_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_52_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_52_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_52_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_52_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_52_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_52_conv_7 = torch.nn.modules.conv.Conv2d(1824, 304, 1, 1, 0, bias=False)
self.features_52_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(304)
self.features_53_conv_0 = torch.nn.modules.conv.Conv2d(304, 1824, 1, 1, 0, bias=False)
self.features_53_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_53_conv_3 = torch.nn.modules.conv.Conv2d(1824, 1824, 3, 1, 1, groups=1824, bias=False)
self.features_53_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1824)
self.features_53_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_53_conv_6_fc_0 = torch.nn.modules.linear.Linear(1824, 80)
self.features_53_conv_6_fc_2 = torch.nn.modules.linear.Linear(80, 1824)
self.features_53_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_53_conv_7 = torch.nn.modules.conv.Conv2d(1824, 512, 1, 1, 0, bias=False)
self.features_53_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_54_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_54_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_54_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_54_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_54_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_54_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_54_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_54_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_54_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_54_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_55_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_55_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_55_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_55_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_55_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_55_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_55_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_55_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_55_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_55_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_56_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_56_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_56_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_56_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_56_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_56_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_56_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_56_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_56_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_56_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_57_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_57_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_57_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_57_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_57_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_57_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_57_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_57_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_57_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_57_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.conv_0 = torch.nn.modules.conv.Conv2d(512, 1792, 1, 1, 0, bias=False)
self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792)
self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1))
self.classifier = torch.nn.modules.linear.Linear(1792, 1000)
def forward(self, input_1):
features_0_0 = self.features_0_0(input_1)
features_0_1 = self.features_0_1(features_0_0)
sigmoid_1 = torch.sigmoid(features_0_1)
mul_1 = features_0_1.__mul__(sigmoid_1)
features_1_conv_0 = self.features_1_conv_0(mul_1)
features_1_conv_1 = self.features_1_conv_1(features_1_conv_0)
sigmoid_2 = torch.sigmoid(features_1_conv_1)
mul_2 = features_1_conv_1.__mul__(sigmoid_2)
features_1_conv_3 = self.features_1_conv_3(mul_2)
features_1_conv_4 = self.features_1_conv_4(features_1_conv_3)
add_1 = mul_1.__add__(features_1_conv_4)
features_2_conv_0 = self.features_2_conv_0(add_1)
features_2_conv_1 = self.features_2_conv_1(features_2_conv_0)
sigmoid_3 = torch.sigmoid(features_2_conv_1)
mul_3 = features_2_conv_1.__mul__(sigmoid_3)
features_2_conv_3 = self.features_2_conv_3(mul_3)
features_2_conv_4 = self.features_2_conv_4(features_2_conv_3)
add_2 = add_1.__add__(features_2_conv_4)
features_3_conv_0 = self.features_3_conv_0(add_2)
features_3_conv_1 = self.features_3_conv_1(features_3_conv_0)
sigmoid_4 = torch.sigmoid(features_3_conv_1)
mul_4 = features_3_conv_1.__mul__(sigmoid_4)
features_3_conv_3 = self.features_3_conv_3(mul_4)
features_3_conv_4 = self.features_3_conv_4(features_3_conv_3)
add_3 = add_2.__add__(features_3_conv_4)
features_4_conv_0 = self.features_4_conv_0(add_3)
features_4_conv_1 = self.features_4_conv_1(features_4_conv_0)
sigmoid_5 = torch.sigmoid(features_4_conv_1)
mul_5 = features_4_conv_1.__mul__(sigmoid_5)
features_4_conv_3 = self.features_4_conv_3(mul_5)
features_4_conv_4 = self.features_4_conv_4(features_4_conv_3)
features_5_conv_0 = self.features_5_conv_0(features_4_conv_4)
features_5_conv_1 = self.features_5_conv_1(features_5_conv_0)
sigmoid_6 = torch.sigmoid(features_5_conv_1)
mul_6 = features_5_conv_1.__mul__(sigmoid_6)
features_5_conv_3 = self.features_5_conv_3(mul_6)
features_5_conv_4 = self.features_5_conv_4(features_5_conv_3)
add_4 = features_4_conv_4.__add__(features_5_conv_4)
features_6_conv_0 = self.features_6_conv_0(add_4)
features_6_conv_1 = self.features_6_conv_1(features_6_conv_0)
sigmoid_7 = torch.sigmoid(features_6_conv_1)
mul_7 = features_6_conv_1.__mul__(sigmoid_7)
features_6_conv_3 = self.features_6_conv_3(mul_7)
features_6_conv_4 = self.features_6_conv_4(features_6_conv_3)
add_5 = add_4.__add__(features_6_conv_4)
features_7_conv_0 = self.features_7_conv_0(add_5)
features_7_conv_1 = self.features_7_conv_1(features_7_conv_0)
sigmoid_8 = torch.sigmoid(features_7_conv_1)
mul_8 = features_7_conv_1.__mul__(sigmoid_8)
features_7_conv_3 = self.features_7_conv_3(mul_8)
features_7_conv_4 = self.features_7_conv_4(features_7_conv_3)
add_6 = add_5.__add__(features_7_conv_4)
features_8_conv_0 = self.features_8_conv_0(add_6)
features_8_conv_1 = self.features_8_conv_1(features_8_conv_0)
sigmoid_9 = torch.sigmoid(features_8_conv_1)
mul_9 = features_8_conv_1.__mul__(sigmoid_9)
features_8_conv_3 = self.features_8_conv_3(mul_9)
features_8_conv_4 = self.features_8_conv_4(features_8_conv_3)
add_7 = add_6.__add__(features_8_conv_4)
features_9_conv_0 = self.features_9_conv_0(add_7)
features_9_conv_1 = self.features_9_conv_1(features_9_conv_0)
sigmoid_10 = torch.sigmoid(features_9_conv_1)
mul_10 = features_9_conv_1.__mul__(sigmoid_10)
features_9_conv_3 = self.features_9_conv_3(mul_10)
features_9_conv_4 = self.features_9_conv_4(features_9_conv_3)
features_10_conv_0 = self.features_10_conv_0(features_9_conv_4)
features_10_conv_1 = self.features_10_conv_1(features_10_conv_0)
sigmoid_11 = torch.sigmoid(features_10_conv_1)
mul_11 = features_10_conv_1.__mul__(sigmoid_11)
features_10_conv_3 = self.features_10_conv_3(mul_11)
features_10_conv_4 = self.features_10_conv_4(features_10_conv_3)
add_8 = features_9_conv_4.__add__(features_10_conv_4)
features_11_conv_0 = self.features_11_conv_0(add_8)
features_11_conv_1 = self.features_11_conv_1(features_11_conv_0)
sigmoid_12 = torch.sigmoid(features_11_conv_1)
mul_12 = features_11_conv_1.__mul__(sigmoid_12)
features_11_conv_3 = self.features_11_conv_3(mul_12)
features_11_conv_4 = self.features_11_conv_4(features_11_conv_3)
add_9 = add_8.__add__(features_11_conv_4)
features_12_conv_0 = self.features_12_conv_0(add_9)
features_12_conv_1 = self.features_12_conv_1(features_12_conv_0)
sigmoid_13 = torch.sigmoid(features_12_conv_1)
mul_13 = features_12_conv_1.__mul__(sigmoid_13)
features_12_conv_3 = self.features_12_conv_3(mul_13)
features_12_conv_4 = self.features_12_conv_4(features_12_conv_3)
add_10 = add_9.__add__(features_12_conv_4)
features_13_conv_0 = self.features_13_conv_0(add_10)
features_13_conv_1 = self.features_13_conv_1(features_13_conv_0)
sigmoid_14 = torch.sigmoid(features_13_conv_1)
mul_14 = features_13_conv_1.__mul__(sigmoid_14)
features_13_conv_3 = self.features_13_conv_3(mul_14)
features_13_conv_4 = self.features_13_conv_4(features_13_conv_3)
add_11 = add_10.__add__(features_13_conv_4)
features_14_conv_0 = self.features_14_conv_0(add_11)
features_14_conv_1 = self.features_14_conv_1(features_14_conv_0)
sigmoid_15 = torch.sigmoid(features_14_conv_1)
mul_15 = features_14_conv_1.__mul__(sigmoid_15)
features_14_conv_3 = self.features_14_conv_3(mul_15)
features_14_conv_4 = self.features_14_conv_4(features_14_conv_3)
sigmoid_16 = torch.sigmoid(features_14_conv_4)
mul_16 = features_14_conv_4.__mul__(sigmoid_16)
size_1 = mul_16.size()
features_14_conv_6_avg_pool = self.features_14_conv_6_avg_pool(mul_16)
view_1 = features_14_conv_6_avg_pool.view(size_1[0], size_1[1])
features_14_conv_6_fc_0 = self.features_14_conv_6_fc_0(view_1)
sigmoid_17 = torch.sigmoid(features_14_conv_6_fc_0)
mul_17 = features_14_conv_6_fc_0.__mul__(sigmoid_17)
features_14_conv_6_fc_2 = self.features_14_conv_6_fc_2(mul_17)
features_14_conv_6_fc_3 = self.features_14_conv_6_fc_3(features_14_conv_6_fc_2)
view_2 = features_14_conv_6_fc_3.view(size_1[0], size_1[1], 1, 1)
mul_18 = mul_16.__mul__(view_2)
features_14_conv_7 = self.features_14_conv_7(mul_18)
features_14_conv_8 = self.features_14_conv_8(features_14_conv_7)
features_15_conv_0 = self.features_15_conv_0(features_14_conv_8)
features_15_conv_1 = self.features_15_conv_1(features_15_conv_0)
sigmoid_18 = torch.sigmoid(features_15_conv_1)
mul_19 = features_15_conv_1.__mul__(sigmoid_18)
features_15_conv_3 = self.features_15_conv_3(mul_19)
features_15_conv_4 = self.features_15_conv_4(features_15_conv_3)
sigmoid_19 = torch.sigmoid(features_15_conv_4)
mul_20 = features_15_conv_4.__mul__(sigmoid_19)
size_2 = mul_20.size()
features_15_conv_6_avg_pool = self.features_15_conv_6_avg_pool(mul_20)
view_3 = features_15_conv_6_avg_pool.view(size_2[0], size_2[1])
features_15_conv_6_fc_0 = self.features_15_conv_6_fc_0(view_3)
sigmoid_20 = torch.sigmoid(features_15_conv_6_fc_0)
mul_21 = features_15_conv_6_fc_0.__mul__(sigmoid_20)
features_15_conv_6_fc_2 = self.features_15_conv_6_fc_2(mul_21)
features_15_conv_6_fc_3 = self.features_15_conv_6_fc_3(features_15_conv_6_fc_2)
view_4 = features_15_conv_6_fc_3.view(size_2[0], size_2[1], 1, 1)
mul_22 = mul_20.__mul__(view_4)
features_15_conv_7 = self.features_15_conv_7(mul_22)
features_15_conv_8 = self.features_15_conv_8(features_15_conv_7)
add_12 = features_14_conv_8.__add__(features_15_conv_8)
features_16_conv_0 = self.features_16_conv_0(add_12)
features_16_conv_1 = self.features_16_conv_1(features_16_conv_0)
sigmoid_21 = torch.sigmoid(features_16_conv_1)
mul_23 = features_16_conv_1.__mul__(sigmoid_21)
features_16_conv_3 = self.features_16_conv_3(mul_23)
features_16_conv_4 = self.features_16_conv_4(features_16_conv_3)
sigmoid_22 = torch.sigmoid(features_16_conv_4)
mul_24 = features_16_conv_4.__mul__(sigmoid_22)
size_3 = mul_24.size()
features_16_conv_6_avg_pool = self.features_16_conv_6_avg_pool(mul_24)
view_5 = features_16_conv_6_avg_pool.view(size_3[0], size_3[1])
features_16_conv_6_fc_0 = self.features_16_conv_6_fc_0(view_5)
sigmoid_23 = torch.sigmoid(features_16_conv_6_fc_0)
mul_25 = features_16_conv_6_fc_0.__mul__(sigmoid_23)
features_16_conv_6_fc_2 = self.features_16_conv_6_fc_2(mul_25)
features_16_conv_6_fc_3 = self.features_16_conv_6_fc_3(features_16_conv_6_fc_2)
view_6 = features_16_conv_6_fc_3.view(size_3[0], size_3[1], 1, 1)
mul_26 = mul_24.__mul__(view_6)
features_16_conv_7 = self.features_16_conv_7(mul_26)
features_16_conv_8 = self.features_16_conv_8(features_16_conv_7)
add_13 = add_12.__add__(features_16_conv_8)
features_17_conv_0 = self.features_17_conv_0(add_13)
features_17_conv_1 = self.features_17_conv_1(features_17_conv_0)
sigmoid_24 = torch.sigmoid(features_17_conv_1)
mul_27 = features_17_conv_1.__mul__(sigmoid_24)
features_17_conv_3 = self.features_17_conv_3(mul_27)
features_17_conv_4 = self.features_17_conv_4(features_17_conv_3)
sigmoid_25 = torch.sigmoid(features_17_conv_4)
mul_28 = features_17_conv_4.__mul__(sigmoid_25)
size_4 = mul_28.size()
features_17_conv_6_avg_pool = self.features_17_conv_6_avg_pool(mul_28)
view_7 = features_17_conv_6_avg_pool.view(size_4[0], size_4[1])
features_17_conv_6_fc_0 = self.features_17_conv_6_fc_0(view_7)
sigmoid_26 = torch.sigmoid(features_17_conv_6_fc_0)
mul_29 = features_17_conv_6_fc_0.__mul__(sigmoid_26)
features_17_conv_6_fc_2 = self.features_17_conv_6_fc_2(mul_29)
features_17_conv_6_fc_3 = self.features_17_conv_6_fc_3(features_17_conv_6_fc_2)
view_8 = features_17_conv_6_fc_3.view(size_4[0], size_4[1], 1, 1)
mul_30 = mul_28.__mul__(view_8)
features_17_conv_7 = self.features_17_conv_7(mul_30)
features_17_conv_8 = self.features_17_conv_8(features_17_conv_7)
add_14 = add_13.__add__(features_17_conv_8)
features_18_conv_0 = self.features_18_conv_0(add_14)
features_18_conv_1 = self.features_18_conv_1(features_18_conv_0)
sigmoid_27 = torch.sigmoid(features_18_conv_1)
mul_31 = features_18_conv_1.__mul__(sigmoid_27)
features_18_conv_3 = self.features_18_conv_3(mul_31)
features_18_conv_4 = self.features_18_conv_4(features_18_conv_3)
sigmoid_28 = torch.sigmoid(features_18_conv_4)
mul_32 = features_18_conv_4.__mul__(sigmoid_28)
size_5 = mul_32.size()
features_18_conv_6_avg_pool = self.features_18_conv_6_avg_pool(mul_32)
view_9 = features_18_conv_6_avg_pool.view(size_5[0], size_5[1])
features_18_conv_6_fc_0 = self.features_18_conv_6_fc_0(view_9)
sigmoid_29 = torch.sigmoid(features_18_conv_6_fc_0)
mul_33 = features_18_conv_6_fc_0.__mul__(sigmoid_29)
features_18_conv_6_fc_2 = self.features_18_conv_6_fc_2(mul_33)
features_18_conv_6_fc_3 = self.features_18_conv_6_fc_3(features_18_conv_6_fc_2)
view_10 = features_18_conv_6_fc_3.view(size_5[0], size_5[1], 1, 1)
mul_34 = mul_32.__mul__(view_10)
features_18_conv_7 = self.features_18_conv_7(mul_34)
features_18_conv_8 = self.features_18_conv_8(features_18_conv_7)
add_15 = add_14.__add__(features_18_conv_8)
features_19_conv_0 = self.features_19_conv_0(add_15)
features_19_conv_1 = self.features_19_conv_1(features_19_conv_0)
sigmoid_30 = torch.sigmoid(features_19_conv_1)
mul_35 = features_19_conv_1.__mul__(sigmoid_30)
features_19_conv_3 = self.features_19_conv_3(mul_35)
features_19_conv_4 = self.features_19_conv_4(features_19_conv_3)
sigmoid_31 = torch.sigmoid(features_19_conv_4)
mul_36 = features_19_conv_4.__mul__(sigmoid_31)
size_6 = mul_36.size()
features_19_conv_6_avg_pool = self.features_19_conv_6_avg_pool(mul_36)
view_11 = features_19_conv_6_avg_pool.view(size_6[0], size_6[1])
features_19_conv_6_fc_0 = self.features_19_conv_6_fc_0(view_11)
sigmoid_32 = torch.sigmoid(features_19_conv_6_fc_0)
mul_37 = features_19_conv_6_fc_0.__mul__(sigmoid_32)
features_19_conv_6_fc_2 = self.features_19_conv_6_fc_2(mul_37)
features_19_conv_6_fc_3 = self.features_19_conv_6_fc_3(features_19_conv_6_fc_2)
view_12 = features_19_conv_6_fc_3.view(size_6[0], size_6[1], 1, 1)
mul_38 = mul_36.__mul__(view_12)
features_19_conv_7 = self.features_19_conv_7(mul_38)
features_19_conv_8 = self.features_19_conv_8(features_19_conv_7)
add_16 = add_15.__add__(features_19_conv_8)
features_20_conv_0 = self.features_20_conv_0(add_16)
features_20_conv_1 = self.features_20_conv_1(features_20_conv_0)
sigmoid_33 = torch.sigmoid(features_20_conv_1)
mul_39 = features_20_conv_1.__mul__(sigmoid_33)
features_20_conv_3 = self.features_20_conv_3(mul_39)
features_20_conv_4 = self.features_20_conv_4(features_20_conv_3)
sigmoid_34 = torch.sigmoid(features_20_conv_4)
mul_40 = features_20_conv_4.__mul__(sigmoid_34)
size_7 = mul_40.size()
features_20_conv_6_avg_pool = self.features_20_conv_6_avg_pool(mul_40)
view_13 = features_20_conv_6_avg_pool.view(size_7[0], size_7[1])
features_20_conv_6_fc_0 = self.features_20_conv_6_fc_0(view_13)
sigmoid_35 = torch.sigmoid(features_20_conv_6_fc_0)
mul_41 = features_20_conv_6_fc_0.__mul__(sigmoid_35)
features_20_conv_6_fc_2 = self.features_20_conv_6_fc_2(mul_41)
features_20_conv_6_fc_3 = self.features_20_conv_6_fc_3(features_20_conv_6_fc_2)
view_14 = features_20_conv_6_fc_3.view(size_7[0], size_7[1], 1, 1)
mul_42 = mul_40.__mul__(view_14)
features_20_conv_7 = self.features_20_conv_7(mul_42)
features_20_conv_8 = self.features_20_conv_8(features_20_conv_7)
add_17 = add_16.__add__(features_20_conv_8)
features_21_conv_0 = self.features_21_conv_0(add_17)
features_21_conv_1 = self.features_21_conv_1(features_21_conv_0)
sigmoid_36 = torch.sigmoid(features_21_conv_1)
mul_43 = features_21_conv_1.__mul__(sigmoid_36)
features_21_conv_3 = self.features_21_conv_3(mul_43)
features_21_conv_4 = self.features_21_conv_4(features_21_conv_3)
sigmoid_37 = torch.sigmoid(features_21_conv_4)
mul_44 = features_21_conv_4.__mul__(sigmoid_37)
size_8 = mul_44.size()
features_21_conv_6_avg_pool = self.features_21_conv_6_avg_pool(mul_44)
view_15 = features_21_conv_6_avg_pool.view(size_8[0], size_8[1])
features_21_conv_6_fc_0 = self.features_21_conv_6_fc_0(view_15)
sigmoid_38 = torch.sigmoid(features_21_conv_6_fc_0)
mul_45 = features_21_conv_6_fc_0.__mul__(sigmoid_38)
features_21_conv_6_fc_2 = self.features_21_conv_6_fc_2(mul_45)
features_21_conv_6_fc_3 = self.features_21_conv_6_fc_3(features_21_conv_6_fc_2)
view_16 = features_21_conv_6_fc_3.view(size_8[0], size_8[1], 1, 1)
mul_46 = mul_44.__mul__(view_16)
features_21_conv_7 = self.features_21_conv_7(mul_46)
features_21_conv_8 = self.features_21_conv_8(features_21_conv_7)
features_22_conv_0 = self.features_22_conv_0(features_21_conv_8)
features_22_conv_1 = self.features_22_conv_1(features_22_conv_0)
sigmoid_39 = torch.sigmoid(features_22_conv_1)
mul_47 = features_22_conv_1.__mul__(sigmoid_39)
features_22_conv_3 = self.features_22_conv_3(mul_47)
features_22_conv_4 = self.features_22_conv_4(features_22_conv_3)
sigmoid_40 = torch.sigmoid(features_22_conv_4)
mul_48 = features_22_conv_4.__mul__(sigmoid_40)
size_9 = mul_48.size()
features_22_conv_6_avg_pool = self.features_22_conv_6_avg_pool(mul_48)
view_17 = features_22_conv_6_avg_pool.view(size_9[0], size_9[1])
features_22_conv_6_fc_0 = self.features_22_conv_6_fc_0(view_17)
sigmoid_41 = torch.sigmoid(features_22_conv_6_fc_0)
mul_49 = features_22_conv_6_fc_0.__mul__(sigmoid_41)
features_22_conv_6_fc_2 = self.features_22_conv_6_fc_2(mul_49)
features_22_conv_6_fc_3 = self.features_22_conv_6_fc_3(features_22_conv_6_fc_2)
view_18 = features_22_conv_6_fc_3.view(size_9[0], size_9[1], 1, 1)
mul_50 = mul_48.__mul__(view_18)
features_22_conv_7 = self.features_22_conv_7(mul_50)
features_22_conv_8 = self.features_22_conv_8(features_22_conv_7)
add_18 = features_21_conv_8.__add__(features_22_conv_8)
features_23_conv_0 = self.features_23_conv_0(add_18)
features_23_conv_1 = self.features_23_conv_1(features_23_conv_0)
sigmoid_42 = torch.sigmoid(features_23_conv_1)
mul_51 = features_23_conv_1.__mul__(sigmoid_42)
features_23_conv_3 = self.features_23_conv_3(mul_51)
features_23_conv_4 = self.features_23_conv_4(features_23_conv_3)
sigmoid_43 = torch.sigmoid(features_23_conv_4)
mul_52 = features_23_conv_4.__mul__(sigmoid_43)
size_10 = mul_52.size()
features_23_conv_6_avg_pool = self.features_23_conv_6_avg_pool(mul_52)
view_19 = features_23_conv_6_avg_pool.view(size_10[0], size_10[1])
features_23_conv_6_fc_0 = self.features_23_conv_6_fc_0(view_19)
sigmoid_44 = torch.sigmoid(features_23_conv_6_fc_0)
mul_53 = features_23_conv_6_fc_0.__mul__(sigmoid_44)
features_23_conv_6_fc_2 = self.features_23_conv_6_fc_2(mul_53)
features_23_conv_6_fc_3 = self.features_23_conv_6_fc_3(features_23_conv_6_fc_2)
view_20 = features_23_conv_6_fc_3.view(size_10[0], size_10[1], 1, 1)
mul_54 = mul_52.__mul__(view_20)
features_23_conv_7 = self.features_23_conv_7(mul_54)
features_23_conv_8 = self.features_23_conv_8(features_23_conv_7)
add_19 = add_18.__add__(features_23_conv_8)
features_24_conv_0 = self.features_24_conv_0(add_19)
features_24_conv_1 = self.features_24_conv_1(features_24_conv_0)
sigmoid_45 = torch.sigmoid(features_24_conv_1)
mul_55 = features_24_conv_1.__mul__(sigmoid_45)
features_24_conv_3 = self.features_24_conv_3(mul_55)
features_24_conv_4 = self.features_24_conv_4(features_24_conv_3)
sigmoid_46 = torch.sigmoid(features_24_conv_4)
mul_56 = features_24_conv_4.__mul__(sigmoid_46)
size_11 = mul_56.size()
features_24_conv_6_avg_pool = self.features_24_conv_6_avg_pool(mul_56)
view_21 = features_24_conv_6_avg_pool.view(size_11[0], size_11[1])
features_24_conv_6_fc_0 = self.features_24_conv_6_fc_0(view_21)
sigmoid_47 = torch.sigmoid(features_24_conv_6_fc_0)
mul_57 = features_24_conv_6_fc_0.__mul__(sigmoid_47)
features_24_conv_6_fc_2 = self.features_24_conv_6_fc_2(mul_57)
features_24_conv_6_fc_3 = self.features_24_conv_6_fc_3(features_24_conv_6_fc_2)
view_22 = features_24_conv_6_fc_3.view(size_11[0], size_11[1], 1, 1)
mul_58 = mul_56.__mul__(view_22)
features_24_conv_7 = self.features_24_conv_7(mul_58)
features_24_conv_8 = self.features_24_conv_8(features_24_conv_7)
add_20 = add_19.__add__(features_24_conv_8)
features_25_conv_0 = self.features_25_conv_0(add_20)
features_25_conv_1 = self.features_25_conv_1(features_25_conv_0)
sigmoid_48 = torch.sigmoid(features_25_conv_1)
mul_59 = features_25_conv_1.__mul__(sigmoid_48)
features_25_conv_3 = self.features_25_conv_3(mul_59)
features_25_conv_4 = self.features_25_conv_4(features_25_conv_3)
sigmoid_49 = torch.sigmoid(features_25_conv_4)
mul_60 = features_25_conv_4.__mul__(sigmoid_49)
size_12 = mul_60.size()
features_25_conv_6_avg_pool = self.features_25_conv_6_avg_pool(mul_60)
view_23 = features_25_conv_6_avg_pool.view(size_12[0], size_12[1])
features_25_conv_6_fc_0 = self.features_25_conv_6_fc_0(view_23)
sigmoid_50 = torch.sigmoid(features_25_conv_6_fc_0)
mul_61 = features_25_conv_6_fc_0.__mul__(sigmoid_50)
features_25_conv_6_fc_2 = self.features_25_conv_6_fc_2(mul_61)
features_25_conv_6_fc_3 = self.features_25_conv_6_fc_3(features_25_conv_6_fc_2)
view_24 = features_25_conv_6_fc_3.view(size_12[0], size_12[1], 1, 1)
mul_62 = mul_60.__mul__(view_24)
features_25_conv_7 = self.features_25_conv_7(mul_62)
features_25_conv_8 = self.features_25_conv_8(features_25_conv_7)
add_21 = add_20.__add__(features_25_conv_8)
features_26_conv_0 = self.features_26_conv_0(add_21)
features_26_conv_1 = self.features_26_conv_1(features_26_conv_0)
sigmoid_51 = torch.sigmoid(features_26_conv_1)
mul_63 = features_26_conv_1.__mul__(sigmoid_51)
features_26_conv_3 = self.features_26_conv_3(mul_63)
features_26_conv_4 = self.features_26_conv_4(features_26_conv_3)
sigmoid_52 = torch.sigmoid(features_26_conv_4)
mul_64 = features_26_conv_4.__mul__(sigmoid_52)
size_13 = mul_64.size()
features_26_conv_6_avg_pool = self.features_26_conv_6_avg_pool(mul_64)
view_25 = features_26_conv_6_avg_pool.view(size_13[0], size_13[1])
features_26_conv_6_fc_0 = self.features_26_conv_6_fc_0(view_25)
sigmoid_53 = torch.sigmoid(features_26_conv_6_fc_0)
mul_65 = features_26_conv_6_fc_0.__mul__(sigmoid_53)
features_26_conv_6_fc_2 = self.features_26_conv_6_fc_2(mul_65)
features_26_conv_6_fc_3 = self.features_26_conv_6_fc_3(features_26_conv_6_fc_2)
view_26 = features_26_conv_6_fc_3.view(size_13[0], size_13[1], 1, 1)
mul_66 = mul_64.__mul__(view_26)
features_26_conv_7 = self.features_26_conv_7(mul_66)
features_26_conv_8 = self.features_26_conv_8(features_26_conv_7)
add_22 = add_21.__add__(features_26_conv_8)
features_27_conv_0 = self.features_27_conv_0(add_22)
features_27_conv_1 = self.features_27_conv_1(features_27_conv_0)
sigmoid_54 = torch.sigmoid(features_27_conv_1)
mul_67 = features_27_conv_1.__mul__(sigmoid_54)
features_27_conv_3 = self.features_27_conv_3(mul_67)
features_27_conv_4 = self.features_27_conv_4(features_27_conv_3)
sigmoid_55 = torch.sigmoid(features_27_conv_4)
mul_68 = features_27_conv_4.__mul__(sigmoid_55)
size_14 = mul_68.size()
features_27_conv_6_avg_pool = self.features_27_conv_6_avg_pool(mul_68)
view_27 = features_27_conv_6_avg_pool.view(size_14[0], size_14[1])
features_27_conv_6_fc_0 = self.features_27_conv_6_fc_0(view_27)
sigmoid_56 = torch.sigmoid(features_27_conv_6_fc_0)
mul_69 = features_27_conv_6_fc_0.__mul__(sigmoid_56)
features_27_conv_6_fc_2 = self.features_27_conv_6_fc_2(mul_69)
features_27_conv_6_fc_3 = self.features_27_conv_6_fc_3(features_27_conv_6_fc_2)
view_28 = features_27_conv_6_fc_3.view(size_14[0], size_14[1], 1, 1)
mul_70 = mul_68.__mul__(view_28)
features_27_conv_7 = self.features_27_conv_7(mul_70)
features_27_conv_8 = self.features_27_conv_8(features_27_conv_7)
add_23 = add_22.__add__(features_27_conv_8)
features_28_conv_0 = self.features_28_conv_0(add_23)
features_28_conv_1 = self.features_28_conv_1(features_28_conv_0)
sigmoid_57 = torch.sigmoid(features_28_conv_1)
mul_71 = features_28_conv_1.__mul__(sigmoid_57)
features_28_conv_3 = self.features_28_conv_3(mul_71)
features_28_conv_4 = self.features_28_conv_4(features_28_conv_3)
sigmoid_58 = torch.sigmoid(features_28_conv_4)
mul_72 = features_28_conv_4.__mul__(sigmoid_58)
size_15 = mul_72.size()
features_28_conv_6_avg_pool = self.features_28_conv_6_avg_pool(mul_72)
view_29 = features_28_conv_6_avg_pool.view(size_15[0], size_15[1])
features_28_conv_6_fc_0 = self.features_28_conv_6_fc_0(view_29)
sigmoid_59 = torch.sigmoid(features_28_conv_6_fc_0)
mul_73 = features_28_conv_6_fc_0.__mul__(sigmoid_59)
features_28_conv_6_fc_2 = self.features_28_conv_6_fc_2(mul_73)
features_28_conv_6_fc_3 = self.features_28_conv_6_fc_3(features_28_conv_6_fc_2)
view_30 = features_28_conv_6_fc_3.view(size_15[0], size_15[1], 1, 1)
mul_74 = mul_72.__mul__(view_30)
features_28_conv_7 = self.features_28_conv_7(mul_74)
features_28_conv_8 = self.features_28_conv_8(features_28_conv_7)
add_24 = add_23.__add__(features_28_conv_8)
features_29_conv_0 = self.features_29_conv_0(add_24)
features_29_conv_1 = self.features_29_conv_1(features_29_conv_0)
sigmoid_60 = torch.sigmoid(features_29_conv_1)
mul_75 = features_29_conv_1.__mul__(sigmoid_60)
features_29_conv_3 = self.features_29_conv_3(mul_75)
features_29_conv_4 = self.features_29_conv_4(features_29_conv_3)
sigmoid_61 = torch.sigmoid(features_29_conv_4)
mul_76 = features_29_conv_4.__mul__(sigmoid_61)
size_16 = mul_76.size()
features_29_conv_6_avg_pool = self.features_29_conv_6_avg_pool(mul_76)
view_31 = features_29_conv_6_avg_pool.view(size_16[0], size_16[1])
features_29_conv_6_fc_0 = self.features_29_conv_6_fc_0(view_31)
sigmoid_62 = torch.sigmoid(features_29_conv_6_fc_0)
mul_77 = features_29_conv_6_fc_0.__mul__(sigmoid_62)
features_29_conv_6_fc_2 = self.features_29_conv_6_fc_2(mul_77)
features_29_conv_6_fc_3 = self.features_29_conv_6_fc_3(features_29_conv_6_fc_2)
view_32 = features_29_conv_6_fc_3.view(size_16[0], size_16[1], 1, 1)
mul_78 = mul_76.__mul__(view_32)
features_29_conv_7 = self.features_29_conv_7(mul_78)
features_29_conv_8 = self.features_29_conv_8(features_29_conv_7)
add_25 = add_24.__add__(features_29_conv_8)
features_30_conv_0 = self.features_30_conv_0(add_25)
features_30_conv_1 = self.features_30_conv_1(features_30_conv_0)
sigmoid_63 = torch.sigmoid(features_30_conv_1)
mul_79 = features_30_conv_1.__mul__(sigmoid_63)
features_30_conv_3 = self.features_30_conv_3(mul_79)
features_30_conv_4 = self.features_30_conv_4(features_30_conv_3)
sigmoid_64 = torch.sigmoid(features_30_conv_4)
mul_80 = features_30_conv_4.__mul__(sigmoid_64)
size_17 = mul_80.size()
features_30_conv_6_avg_pool = self.features_30_conv_6_avg_pool(mul_80)
view_33 = features_30_conv_6_avg_pool.view(size_17[0], size_17[1])
features_30_conv_6_fc_0 = self.features_30_conv_6_fc_0(view_33)
sigmoid_65 = torch.sigmoid(features_30_conv_6_fc_0)
mul_81 = features_30_conv_6_fc_0.__mul__(sigmoid_65)
features_30_conv_6_fc_2 = self.features_30_conv_6_fc_2(mul_81)
features_30_conv_6_fc_3 = self.features_30_conv_6_fc_3(features_30_conv_6_fc_2)
view_34 = features_30_conv_6_fc_3.view(size_17[0], size_17[1], 1, 1)
mul_82 = mul_80.__mul__(view_34)
features_30_conv_7 = self.features_30_conv_7(mul_82)
features_30_conv_8 = self.features_30_conv_8(features_30_conv_7)
add_26 = add_25.__add__(features_30_conv_8)
features_31_conv_0 = self.features_31_conv_0(add_26)
features_31_conv_1 = self.features_31_conv_1(features_31_conv_0)
sigmoid_66 = torch.sigmoid(features_31_conv_1)
mul_83 = features_31_conv_1.__mul__(sigmoid_66)
features_31_conv_3 = self.features_31_conv_3(mul_83)
features_31_conv_4 = self.features_31_conv_4(features_31_conv_3)
sigmoid_67 = torch.sigmoid(features_31_conv_4)
mul_84 = features_31_conv_4.__mul__(sigmoid_67)
size_18 = mul_84.size()
features_31_conv_6_avg_pool = self.features_31_conv_6_avg_pool(mul_84)
view_35 = features_31_conv_6_avg_pool.view(size_18[0], size_18[1])
features_31_conv_6_fc_0 = self.features_31_conv_6_fc_0(view_35)
sigmoid_68 = torch.sigmoid(features_31_conv_6_fc_0)
mul_85 = features_31_conv_6_fc_0.__mul__(sigmoid_68)
features_31_conv_6_fc_2 = self.features_31_conv_6_fc_2(mul_85)
features_31_conv_6_fc_3 = self.features_31_conv_6_fc_3(features_31_conv_6_fc_2)
view_36 = features_31_conv_6_fc_3.view(size_18[0], size_18[1], 1, 1)
mul_86 = mul_84.__mul__(view_36)
features_31_conv_7 = self.features_31_conv_7(mul_86)
features_31_conv_8 = self.features_31_conv_8(features_31_conv_7)
add_27 = add_26.__add__(features_31_conv_8)
features_32_conv_0 = self.features_32_conv_0(add_27)
features_32_conv_1 = self.features_32_conv_1(features_32_conv_0)
sigmoid_69 = torch.sigmoid(features_32_conv_1)
mul_87 = features_32_conv_1.__mul__(sigmoid_69)
features_32_conv_3 = self.features_32_conv_3(mul_87)
features_32_conv_4 = self.features_32_conv_4(features_32_conv_3)
sigmoid_70 = torch.sigmoid(features_32_conv_4)
mul_88 = features_32_conv_4.__mul__(sigmoid_70)
size_19 = mul_88.size()
features_32_conv_6_avg_pool = self.features_32_conv_6_avg_pool(mul_88)
view_37 = features_32_conv_6_avg_pool.view(size_19[0], size_19[1])
features_32_conv_6_fc_0 = self.features_32_conv_6_fc_0(view_37)
sigmoid_71 = torch.sigmoid(features_32_conv_6_fc_0)
mul_89 = features_32_conv_6_fc_0.__mul__(sigmoid_71)
features_32_conv_6_fc_2 = self.features_32_conv_6_fc_2(mul_89)
features_32_conv_6_fc_3 = self.features_32_conv_6_fc_3(features_32_conv_6_fc_2)
view_38 = features_32_conv_6_fc_3.view(size_19[0], size_19[1], 1, 1)
mul_90 = mul_88.__mul__(view_38)
features_32_conv_7 = self.features_32_conv_7(mul_90)
features_32_conv_8 = self.features_32_conv_8(features_32_conv_7)